Systems for processing digital representations of images are commonly used to process data representing surfaces such as digital elevation models (DEMs). A DEM is a digital map of the elevation of an area on the earth. The data is collected by any well-known means such as LADAR (Laser Detection And Ranging), or by IFSAR (Interferometric Synthetic Aperture Radar) or the like. In operation, the LADAR instrument transmits light to a target. The transmitted light interacts with and is changed by the target. Some of this light is reflected or scattered back to the sensor of the transmitting instrument where it is detected, stored, and analyzed. The change in the properties of the light enables some properties of the target to be determined. The time required for light to travel to the target and back to the LADAR instrument is used to determine the range to the target. IFSAR is used to ingest and process high-resolution elevation data produced through a technique called radar interferometry.
As in the case of LADAR, IFSAR produces data useful for extracting DEMs.
Digital elevation models (DEMs) may be represented as a height map through gray scale images wherein the pixel values are actually terrain elevation values. The pixels are also correlated to world space (longitude and latitude), and each pixel represents some variable volume of that space depending on the purpose of the model and land area depicted.
Referring to FIG. 1 there is shown an example of an airborne LADAR system 100. The system comprises a LADAR instrument 102 mounted on an aircraft 104. Below the aircraft is an area 107 comprising the ground and a canopy formed by trees and other foliage obstructing the view of the ground (earth) from an aerial view. The LADAR instrument 102 emits a plurality of laser light pulses which are directed toward the ground. The LADAR instrument 102 comprises a sensor 103 that detects the reflections/scattering of the pulses. The LADAR instrument 102 provides 3-D data including elevation (Z) versus position (X,Y) information from a single frame. It should be noted, however, that multiple frames of portions of the area from different perspectives are used to generate a composite image. The tree canopy overlying the terrain results in significant obscuration of targets (e.g. vehicle 106) under that tree canopy. The points received by the sensor 103 of instrument 102 from the ground and the target 106 are thus sparse. Hence, a robust system for processing the points is required. Moreover, to be of the most value, an image of the ground wherein the target 106 can be perceived easily must be available quickly.
Extraction of data points generated by LADAR to produce a DEM is known. However, such methods are computationally intensive, and where a large number of data points are processed, run-time applications can be difficult and/or slow. Therefore, there is a need for more efficient methods and systems for production of DEMs using topological data points. In order to be sensitive enough to detect and discern scene content under heavy obscuration (trees, camouflage netting, etc.), the sensor should be able to trigger on single photons.
Once point data such as that collected by a LADAR sensor is processed there still remains the task of identification of the objects in the scene. In the situation discussed above, the task may be to identify the type and model of a hidden vehicle. The output of the processing of the raw point data may still be a cloud of points that may not provide sufficient information for a human to identify. Moreover, there may be a huge amount of data required to provide an output that really takes the guesswork (intuition) out of identifying the target.
For manual identification of vehicles under heavy cover from LADAR point data, several significant challenges arise when exploiting the data within a visualization tool. Such a tool should (1) compare a high voxel resolution cloud of point data to a very detailed model (high polygonal facet count) to verify ID; (2) detect differences between the point data and the model (new features, add-on or relocated parts, etc.); (3) focus on data in the vicinity of the object to be identified and make the rest of the data disappear; and (4) strip away close obscurants (camouflage netting, low foliage, and the like). Therefore, there is a need for an improved solution to the object identification problem that satisfies the above requirements.